本研究著重在考慮電壓控制、再生能源、能源安全、資訊安全、碳交易與動態差異定價之配電網路設計模型的發展。本研究之目的為在不確定與模糊參數下最佳化淨利潤。不確定參數包括發電量與成本;再生能源與/或資訊安全技術投資、能源生產與配電營運費用以及未滿足消費需求之懲罰。目的為決定再生能源技術與發電量、對每個集群消費者團體與個人消費者、電力公司提供之動態差異定價、訊息安全與配電網路設計相結合之努力程度。為了控制能源消費者之需求，電力公司根據一天中的高低能源消耗時數制定動態價格，並根據集群消費者需求回應程度制定差異價格。本研究單獨與混和採用模糊方法與隨機方法產生模糊隨機規劃來解決在幾個參數中觀察到的模糊與概率的不確定性。為解決此配電網路設計問題，本研究開發了兩個模型。第一個模型將兩種顯著的定價方案，即差異定價與動態定價，整合為一個統一的動態差異定價，其中兩種定價方案也同時適用。在該模型數值分析中可以看出，再生能源的發電量與利潤隨著信賴區間降低而增加。第二個模型著重於透過資訊安全技術來最佳化電力公司之利潤，以降低由於不安全數據傳輸所造成之營運成本與損失，以及控制消費者需求反應的動態差異定價。第二個模型之數據分析顯示，作業取代的增加會增加建立訊息安全之努力，因此可以發現淨利潤之增加，因為它以指數降低營運成本且高度降低數據遺失所產生之成本。然而，當作業取代率高於70%時，淨利潤會因為建立訊息安全的投資成本過高而下降。

The study focuses on the development of power distribution network design models considering distributed renewable energy resources, energy security, information security, carbon trading and dynamic-differential prices. Researches are lacking to integrate dynamic pricing and differential pricing though they are addressed separately in the power distribution network design. The aim of this study is to optimize the net profit with the assumptions of uncertain and fuzzy parameters including power generation capacities, several costs and most importantly energy demand of customers. The purpose was to determine the renewable energy resources technologies and their respective capacities, the actual energy supply to each clustered consumers groups and individual consumers, the dynamic-differential prices offered by the power company, the level of effort to incorporate information security with power distribution network design. Fuzzy approach and stochastic are employed either separately or being hybridized to form a fuzzy stochastic programming to address both fuzzy and probabilistic uncertainties observed in several parameters. To address the power distribution network design problems, two models are developed in this thesis. The first model basically integrated the two prominent pricing schemes, the differential pricing and the dynamic pricing, into a unified dynamic-differential pricing where both pricing practices are applied simultaneously. In this model, it observed from the numerical analysis that generation capacities of the renewable energy resources and the profit increase with a decrease in the confidence level. The second model focus on optimizing the profit of the power company with an introduction of information security technology to reduce operational costs and losses due to unsecured data transfer in addition to the dynamic-differential pricing that controls the demand response of customers. The numerical analysis for the later model shows that the increase in the rate operational replacement increases in the effort to establish the information security and hence an increase in the net profit is observed because it decreases the operational cost exponentially and highly lowers the cost incurred from data loss. However, when the rate of operational replacement goes higher than 60%, the net profit declines due to the high investment costs to establish the information security. Therefore, to control the demand of energy consumers, power company set dynamic prices based on their hourly consumption and differential prices to cluster customers based on their level of demand responses. In addition, a power company can also set an α value (confidence level) and decide on the rate of operational replacement value to optimizes its profit while addressing the demand responses of its customers or secure the energy demanded.

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